# Pandas: Complex aggregation expressions

```
>>> df = pd.DataFrame(np.random.randn(10,3),columns=list('ABC'))
>>> df['D'] = [1, 1, 1, 2, 2, 2, 3, 3, 3, 3, ]
# Define the aggregation calculations
>>> aggregations = {
# Work on the "A" column.
'A': {
'total_A': 'sum', # get the sum, and call this result 'total_A'
'average_A': 'mean', # get mean, call result 'average_A'
'num_A': 'count'
},
# Work on the "B" column.
'B': {
'max_B': 'max', # Find the max, call the result "max_B"
'min_B': 'min',
'range_B': lambda x: max(x) - min(x)
},
# Calculate two results for the 'C' column with a list of aggregation functions.
'C': ["count", "max"]
}
# Perform groupby aggregation by column "D".
>>> df.groupby('D').agg(aggregations)
B C A
min_B max_B range_B count max average_A total_A num_A
D
1 -0.986305 1.478065 2.464370 3 -0.158469 -0.516790 -1.550369 3
2 0.390151 1.661266 1.271115 3 1.150179 -0.765209 -2.295627 3
3 -0.658708 1.769680 2.428388 4 0.875891 0.007827 0.031307 4
```

Via shanelynn.ie .

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